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Key Feature Extraction Methods

Feature extraction methods are applied differently depending on the model's structure.


1. Feature Engineering​

In the early days of machine learning, people manually analyzed data to select useful features, performing feature engineering.

Feature engineering is performed using the following methods depending on the machine learning model:

  • Image Processing: Edge detection, color histogram analysis

  • Natural Language Processing: Word frequency, sentence length

Feature engineering is crucial for enhancing model performance, but as data complexity increased, automated methods became necessary.


2. Utilizing Deep Learning​

In deep learning, neural networks receive input data and automatically learn features. Representative methods include:

  • CNN (Convolutional Neural Network): Automatically extracts features like edges, textures, and shapes from images

  • RNN (Recurrent Neural Network): Learns patterns over time to analyze speech or text data

  • Transformer: Analyzes context in natural language processing to identify important words

Deep learning learns various features on its own based on the complexity of the data, eliminating the need for manual feature selection.


Feature Extraction and Neural Network Architecture​

The feature extraction process is closely related to the layers of a neural network.

  • Input Layer: Directly receives the original data

  • Convolutional Layer: Uses filters to extract localized features

  • Pooling Layer: Removes unnecessary information, retaining only important features

  • Fully Connected Layer: Ultimately performs classification or prediction based on the features

In CNNs, for example, the initial convolutional layers detect simple lines and edges, while deeper layers learn object shapes and complex patterns.

Feature Extraction Using CNN
Initial Layers: Detect lines and edges
Intermediate Layers: Learn complex patterns
Final Layers: Identify shapes of entire objects

As the layers become deeper, the model learns increasingly abstract features, enhancing its expressive power.


Why Feature Extraction is Important​

Feature extraction is a crucial process that determines the performance of machine learning and deep learning models. With well-extracted features, models can maintain high accuracy with less data and improve learning speed.

Additionally, analyzing the features a neural network learns can enhance the model's explainability.

For example, visualizing which parts of an image a CNN emphasizes helps understand the criteria the model uses for its decisions.


Feature extraction is an essential step for AI models to effectively learn from data.

In the next lesson, we'll explore how the depth of hidden layers affects model performance.

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